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Accurately predicting clinical outcome of aneurysmal subarachnoid hemorrhage (aSAH) patients is difficult. The purpose of this study was to develop and test a new fully-automated computer-aided detection (CAD) scheme of brain computed tomography (CT) images to predict prognosis of aSAH patients. A retrospective dataset of 59 aSAH patients was assembled. Each patient had 2 sets of CT images acquired at admission and prior-to-discharge. CAD scheme was applied to segment intracranial brain regions into four subregions, namely, cerebrospinal fluid (CSF), white matter (WM), gray matter (GM), and leaked extraparenchymal blood (EPB), respectively. CAD then detects sulci and computes 9 image features related to 5 volumes of the segmented sulci, EPB, CSF, WM, and GM and 4 volumetrical ratios to sulci. Subsequently, applying a leave-one-case-out cross-validation method embedded with a principal component analysis (PCA) algorithm to generate optimal feature vector, 16 support vector machine (SVM) models were built using CT images acquired either at admission or prior-to-discharge to predict each of eight clinically relevant parameters commonly used to assess patients' prognosis. Finally, a receiver operating characteristics (ROC) method was used to evaluate SVM model performance. Areas under ROC curves of 16 SVM models range from 0.62 ± 0.07 to 0.86 ± 0.07. In general, SVM models trained using CT images acquired at admission yielded higher accuracy to predict short-term clinical outcomes, while SVM models trained using CT images acquired prior-to-discharge demonstrated higher accuracy in predicting long-term clinical outcomes. This study demonstrates feasibility to predict prognosis of aSAH patients using new quantitative image markers generated by SVM models.

ObjectivesThe purpose of this pilot study was to obtain baseline quantitative pupillometry (QP) measurements before and after catheter-directed cerebral angiography (DCA) to explore the hypothesis that cerebral angiography is an independent predictor of change in pupillary light reflex (PLR) metrics.DesignThis was a prospective, observational pilot study of PLR assessments obtained using QP 30 min before and after DCA. All patients had QP measurements performed with the NPi-300 (Neuroptics) pupillometer.SettingRecruitment was done at a single-centre, tertiary-care academic hospital and comprehensive stroke centre in Dallas, Texas.ParticipantsFifty participants were recruited undergoing elective or emergent angiography. Inclusion criteria were a physician-ordered interventional neuroradiological procedure, at least 18 years of age, no contraindications to PLR assessment with QP, and nursing transport to and from DCA. Patients with a history of eye surgery were excluded.Main outcome measuresDifference in PLR metric obtained from QP 30 min before and after DCA.ResultsStatistically significant difference was noted in the pre and post left eye readings for the minimum pupil size (a.k.a., pupil diameter on maximum constriction). The mean maximum constriction diameter prior to angiogram of 3.2 (1.1) mm was statistically larger than after angiogram (2.9 (1.0) mm; p<0.05); however, this was not considered clinically significant. Comparisons for all other PLR metrics pre and post angiogram demonstrated no significant difference. Using change in NPi pre and post angiogram (Δpre=0.05 (0.77) vs Δpost=0.08 (0.67); p=0.62), we calculated the effect size as 0.042. Hence, detecting a statistically significant difference in NPi, if a difference exists, would require a sample size of ~6000 patients.ConclusionsOur study provides supportive data that in an uncomplicated angiogram, even with intervention, there is no effect on the PLR.

Stress-induced hyperglycemia (SIH) is a neuroendocrine response to acute illness. Although SIH has an adverse association with intracerebral hemorrhage (ICH), quantitative measures and determinants of SIH are not well delineated. In the present study, we objectively evaluated SIH using glycemic gap (GG) and identified its radiological and clinical determinants, with a 5-year retrospective review of charts of ICH patients. We calculated GG using the regression equation (GG = AG −28.7 × HbA1c + 46.7) and evaluated whether GG is an independent predictor of mortality using a multivariate regression model. Radiological volumes of different intracranial compartments were determined using image segmentation software. We correlated GG with different clinical and radiological parameters using Pearson correlation coefficient (PCC), Spearman’s rank correlation (SRC), and Wilcoxon rank sum test. Then, we calculated the value of GG associated with mortality. Out of 328 patients, 238 (73%) survived hospitalization and 90 (27%) expired. GG was found to be an independent predictor of mortality ( r =0.008, p =0.04). Additionally, GG was positively correlated with intraparenchymal hemorrhage (IPH) volume (PCC=0.185, p <0.01) and intraventricular hemorrhage (IVH) volume (PCC=0.233, p <0.01) and negatively correlated with cerebrospinal fluid (CSF) volume (PCC=−0.151, p <0.01) and brain tissue volume (PCC=-0.099, p =0.08). GG was positively correlated with patients’ ICH score (SRC=0.377, p <0.01), Glasgow Coma Scale (GCS) (PCC=−0.356, p <0.01), hydrocephalus ( p <0.01), and IVH in the third ventricle ( p <0.01). The univariate logistic regression model identified 30.0 mg/dl as the value of GG (AUC=0.655, p <0.01) that predicted mortality with 52.2% sensitivity and 75.2% specificity and defined SIH. In conclusion, GG independently predicts mortality in ICH patients and positively correlates with IPH and IVH volumes. However, causality between the two is not established and would require specifically designed studies.

Post-hemorrhage period after aneurysmal subarachnoid hemorrhage (aSAH) has several systemic manifestations including prothrombotic and pro-inflammatory states. Inter-relationship between these states using established/routine laboratory biomarkers and its long-term effect on clinical outcome is not well-defined. Retrospective analysis of prospective cohort of 44 aSAH patients. Trend of procoagulant biomarkers [coated-platelets, mean platelet volume to platelet count (MPV:PLT)] and peripheral inflammatory biomarkers [platelet-lymphocyte ratio (PLR), neutrophil-platelet ratio (NLR)] were analyzed using regression analysis. Occurrence of delayed cerebral ischemia (DCI), modified Rankin score (mRS) of 3–6 and Montreal cognitive assessment (MoCA) of <26 at 1-year defined adverse clinical outcome. Patients with worse mRS and MoCA score had higher rise in coated-platelet compared to those with better scores [20.4 (IQR: 15.6, 32.9) vs. 10.95 (IQR: 6.1, 18.9), p = 0.003] and [16.9 (IQR: 13.4, 28.1) vs. 10.95 (IQR: 6.35, 18.65), p = 0.02] respectively. NLR and PLR trends showed significant initial decline followed by a gradual rise in NLR among those without DCI as compared to persistent low levels in those developing DCI (0.13 units/day vs. -0.07 units/day, p = 0.06). Coated-platelet rise after aSAH is associated with adverse long-term clinical outcome. NLR and PLR trends show an early immune-depressed state after aSAH. •Coated-platelets and MPV:PLT, markers of procoagulant state shows an early rise after aneurysmal subarachnoid hemorrhage•Blood and cerebrospinal fluid volumes show correlation with adverse clinical outcome in a “dose dependent” manner•NLR, PLR trends show immune-depressed state during early phase after aneurysmal subarachnoid hemorrhage

Metabolomics may identify biomarkers for acute ischemic stroke (AIS). Previously, circulating metabolites were compared in AIS and healthy controls without accounting for stroke size. The goal of this study was to identify metabolites that associate with the volume of AIS. We prospectively analyzed 1554 serum metabolites in the acute (72 h) and chronic (3–6 months) stages of 60 ischemic stroke patients. We calculated infarct volume using diffusion-weighted images with MR segmentation software and associated the volume with stage-specific metabolites, acute-to-chronic stage changes, and multiple mixed regression in metabolite concentrations using multivariate regression analysis. We used the two-stage Benjamini and Hochberg (TSBH) procedure for multiple testing. Four unknown metabolites at the acute stage significantly associated with infarct volume: X24541, X24577, X24581, and X2482 (all p  < 0.01). Nine metabolites at the chronic stage are significantly associated with infarct volume: indolpropinate, alpha ketoglutaramate, picolinate, X16087, X24637, X24576, X24577, X24582, X24581 (all p  < 0.048). Infarct volume is also associated with significant changes in serum concentrations of twenty-seven metabolites, with p values from 0.01 to 1.48 × 10 −7 , and on five metabolites using mixed regression model. This prospective pilot study identified several metabolites associated with the volume of ischemic infarction. Confirmation of these findings on a larger dataset would help characterize putative pathways underlying the size of ischemic infarction and facilitate the identification of biomarkers or therapeutic targets.

One third of patients with intracerebral hemorrhage (ICH) require mechanical ventilation; in most, tracheostomy may be necessary. Limited data exist about predictors of tracheostomy in ICH. The aim of our study is to identify predictors of tracheostomy in ICH. We reviewed medical records of patients seen in our institution between 2005 and 2009, using ICD-9 codes for ICH, for admission clinical and radiological parameters. A stepwise logistic regression model was used to identify tracheostomy predictors. Ninety patients with ICH were included in the analysis, eleven of which required tracheostomy. Patients requiring a tracheostomy were more likely to have a large hematoma volume (≥30mL) (63.4% vs. 29.1%, p=0.037), intraventricular hemorrhage (81.8% vs. 27.8%, p<0.0001), hydrocephalus (81.8% vs. 8.8%, p<0.0001), admission GCS<8 (81.8% vs. 5.1%, p<0.0001), intubation≥14 days (54.5% vs. 1.27%, p<0.0001) and pneumonia (63.6% vs. 17.7%, p=0.003). Stepwise logistic regression yielded admission GCS (OR=80.55, p=0.0003) and intubation days (OR=87.49, p<0.006) as most important predictors. We could potentially predict the need for tracheostomy early in the course of ICH based on the admission GCS score; duration of intubation is another predictor for tracheostomy. Early tracheostomy could decrease the time, and therefore risks of prolonged endotracheal intubation and length of hospital stay.

Acute phase after aneurysmal subarachnoid hemorrhage (aSAH) is associated with several metabolic derangements including stress-induced hyperglycemia (SIH). The present study is designed to identify objective radiological determinants for SIH to better understand its contributory role in clinical outcomes after aSAH. A computer-aided detection tool was used to segment admission computed tomography (CT) images of aSAH patients to estimate intracranial blood and cerebrospinal fluid volumes. Modified Graeb score (mGS) was used as a semi-quantitative measure to estimate degree of hydrocephalus. The relationship between glycemic gap (GG) determined SIH, mGS, and estimated intracranial blood and cerebrospinal fluid volumes were evaluated using linear regression. Ninety-four [94/187 (50.3%)] among the study cohort had SIH (defined as GG > 26.7 mg/dl). Patients with SIH had 14.3 ml/1000 ml more intracranial blood volume as compared to those without SIH [39.6 ml (95% confidence interval, CI, 33.6 to 45.5) vs. 25.3 ml (95% CI 20.6 to 29.9), p  = 0.0002]. Linear regression analysis of mGS with GG showed each unit increase in mGS resulted in 1.2 mg/dl increase in GG [ p  = 0.002]. Patients with SIH had higher mGS [median 4.0, interquartile range, IQR 2.0–7.0] as compared to those without SIH [median 2.0, IQR 0.0–6.0], p  = 0.002. Patients with third ventricular blood on admission CT scan were more likely to develop SIH [67/118 (56.8%) vs. 27/69 (39.1%), p  = 0.023]. Hence, the present study, using unbiased SIH definition and objective CT scan parameters, reports “dose-dependent” radiological features resulting in SIH. Such findings allude to a brain injury-stress response-neuroendocrine axis in etiopathogenesis of SIH.

Aneurysmal subarachnoid hemorrhage (aSAH) is associated with high socio-economic burden. Prothrombotic states of early brain injury (EBI) and delayed cerebral ischemia (DCI) after aSAH determine morbidity and mortality. To understand how activated platelets might contribute to such prothrombotic states, we studied trends in coated-platelets during EBI and DCI periods. Serial blood samples from a prospective cohort of aSAH patients were collected and assayed for coated-platelet levels. Patient’s coated-platelet level during post-hospital discharge follow-up served as an estimate of baseline. Occurrence of DCI, Montreal cognitive assessment (MOCA) score of < 26, and modified Rankin scale (mRS) of 3–6 were considered poor clinical outcomes. Non-linear regression analysis detected a transition between periods of rising and declining coated-platelet levels at day 4. Additional regression analyses of coated-platelet trends before day 4 showed differences among patients with modified Fisher 3–4 [4.2% per day (95% CI 2.4, 6.1) vs. − 0.8% per day (95% CI − 3.4, 1.8); p  = 0.0023] and those developing DCI [4.6% per day (95% CI 2.8, 6.5) vs. − 1.9% per day (95% CI − 4.5, 0.5); p  < 0.001]. Differences between peak coated-platelet levels and baseline levels were larger, on average for those with DCI [18.1 ± 9.6 vs. 10.6 ± 8.0; p  = 0.03], MOCA < 26 [17.0 ± 7.8 vs. 10.7 ± 7.4; p  = 0.05] and mRS 3–6 [24.8 ± 10.5 vs. 11.9 ± 7.6; p  = 0.01]. Coated-platelet trends after aSAH predict DCI and short-term clinical outcomes. The degree of rise in coated-platelets is also associated with adverse clinical outcomes.